Economic evaluations of big data analytics for clinical decision-making: a scoping review

Bakker, L. J.; Aarts, Jos; Groot, Carin A. Uyl–de; Redekop, William K.

doi:10.1093/jamia/ocaa102

Cited by 19 publications

(19 citation statements)

References 99 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bakker et al expressed that the terms "advanced", "best practice", or "traditional" are time-dependent. Therefore, the definition of big data should encompass a list of analytics that are regarded as advanced or best practice, which is time dependant and updated regularly (Bakker et al, 2020). Big data in healthcare needs to be assessed, updated, and interpreted periodically to ensure maximum output.…”

Section: Big Data and Predictive Analytics In Healthcarementioning

confidence: 99%

“…This type of analytics can be used to predict outbreaks and epidemics, identify communities or individuals in need of health services, or predict load default or forecasting weather. The Chicago Department of Public Health used these analytics to develop microtargeting campaigns for mammography screening ( Bakker et al., 2020 ). Prescriptive Analytics: Prescriptive analytics help to undertake the best possible course of action based on information learned through descriptive and predictive analysis.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

View full text Add to dashboard Cite

Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

show abstract

Section: Big Data and Predictive Analytics In Healthcarementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

View full text Add to dashboard Cite

show abstract

“…Chen and See also performed a review published in the JMIR and found several similar limitations [ 135 ]. In July of 2020, Bakker et al from the Netherlands published a systematic review of the health and economic impact of big data analytics for clinical decision making (not specific to COVID-19) in the JAMIA [ 136 ]. They reviewed 12,311 papers for eligibility and found only seven papers that reported cost-savings and improved outcomes from implemented “big data analytics” models.…”

Section: Artificial Intelligence In Diagnosis and Predictive Analyticsmentioning

confidence: 99%

The Clinical Information Systems Response to the COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

Summary Objective: The year 2020 was predominated by the coronavirus disease 2019 (COVID-19) pandemic. The objective of this article is to review the areas in which clinical information systems (CIS) can be and have been utilized to support and enhance the response of healthcare systems to pandemics, focusing on COVID-19. Methods: PubMed/MEDLINE, Google Scholar, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies pertaining to CIS, pandemics, and COVID-19 through October 2020. The most informative and detailed studies were highlighted, while many others were referenced. Results: CIS were heavily relied upon by health systems and governmental agencies worldwide in response to COVID-19. Technology-based screening tools were developed to assist rapid case identification and appropriate triaging. Clinical care was supported by utilizing the electronic health record (EHR) to onboard frontline providers to new protocols, offer clinical decision support, and improve systems for diagnostic testing. Telehealth became the most rapidly adopted medical trend in recent history and an essential strategy for allowing safe and effective access to medical care. Artificial intelligence and machine learning algorithms were developed to enhance screening, diagnostic imaging, and predictive analytics - though evidence of improved outcomes remains limited. Geographic information systems and big data enabled real-time dashboards vital for epidemic monitoring, hospital preparedness strategies, and health policy decision making. Digital contact tracing systems were implemented to assist a labor-intensive task with the aim of curbing transmission. Large scale data sharing, effective health information exchange, and interoperability of EHRs remain challenges for the informatics community with immense clinical and academic potential. CIS must be used in combination with engaged stakeholders and operational change management in order to meaningfully improve patient outcomes. Conclusion: Managing a pandemic requires widespread, timely, and effective distribution of reliable information. In the past year, CIS and informaticists made prominent and influential contributions in the global response to the COVID-19 pandemic.

show abstract

“…Future studies should also look into developing a proper sports economics' frameworks, inspired by health economics [42,127,151,569], which would include the time and nancial cost of given measurement technology compared to its expected value for athletic performance.…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

View full text Add to dashboard Cite

In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

show abstract

Economic evaluations of big data analytics for clinical decision-making: a scoping review

Cited by 19 publications

References 99 publications

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

The Clinical Information Systems Response to the COVID-19 Pandemic

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Contact Info

Product

Resources

About